Open AccessDetection of weekly cycles in atmospheric data
Author(s) -
Jolliffe I. T.
Publication year - 2017
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.1646
Subject(s) - histogram , computer science , environmental science , atmospheric research , rank (graph theory) , construct (python library) , climatology , meteorology , mathematics , artificial intelligence , geology , geography , combinatorics , image (mathematics) , programming language
There is considerable interest in whether weekly cycles are present in certain types of atmospheric and hydrologic data. Seven days is not a natural period for cyclic behaviour, but rather a human construct. Hence the presence of weekly cycles can help to confirm the presence and nature of anthropogenic climate change. Several statistical tests have been used to investigate the presence of such cycles. In this short note, a test previously used for ‘flatness’ of rank histograms in ensemble forecasting is shown to be useful for this task for certain types of data, with some advantages compared with existing techniques. Some disadvantages and extensions are also described.